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What is Neataptic?

Neataptic is thé Javascript neural network solution. Not only does it allow you to create neural networks on-the-go, it also allows you to visualise your networks to understand what's really going on. An important aspect that neataptic introduces in comparison with its competitors is neuro-evolution. You can evolve neural networks through a process described in the NEAT paper.

This allows compability with all kinds of projects; training an XOR gate, controlling a robot or an image classifier.

Genetic neural network example

This is an example of the creation and loop of a genetic neural network. The goal of this genetic algorithm is too create a population that will output a value that is as high as possible (=1) when inputted 0. Please note that this is just an example, and this problem is much easier to 'solve' by backpropagating.

/** The XOR training set */
var trainingSet = [
  { input: [0, 0], output: [0] },
  { input: [0, 1], output: [1] },
  { input: [1, 0], output: [1] },
  { input: [1, 1], output: [0] },
];

/** Set up the built-in genetic algorithm */
var neat = new Neat(2, 1, fitnessFunction, {
  mutationAmount: 3,
  mutationRate: 0.6,
  equal: true
});

/** Fitness function, calculate the error */
function fitnessFunction(genome){
  return -genome.test(trainingSet, Methods.Cost.MSE).error;
}

/** The main loop */
function loop(){
  neat.evolve();
  var fittest = neat.getFittest();

  if(fittest.score < -0.03 && neat.generation < 500){
   setTimeout(loop, 100);
  }
};

Run it here!

If you want to know how to set up one of these algorithms yourself, feel free to take a look at the wiki pages! If you want to implement a genetic neural network algorithm, but don't know how, feel free to contact me at wagenaartje@protonmail.com!